Removing multiple degradations, such as haze, rain, and blur, from real-world images poses a challenging and illposed problem. Recently, unified models that can handle different degradations have been proposed and yield promising results. However, these approaches focus on synthetic images and experience a significant performance drop when applied to realworld images. In this paper, we introduce Uni-Removal, a twostage semi-supervised framework for addressing the removal of multiple degradations in real-world images using a unified model and parameters. In the knowledge transfer stage, Uni-Removal leverages a supervised multi-teacher and student architecture in the knowledge transfer stage to facilitate learning from pretrained teacher networks specialized in different degradation types. A multi-grained contrastive loss is introduced to enhance learning from feature and image spaces. In the domain adaptation stage, unsupervised fine-tuning is performed by incorporating an adversarial discriminator on real-world images. The integration of an extended multi-grained contrastive loss and generative adversarial loss enables the adaptation of the student network from synthetic to real-world domains. Extensive experiments on real-world degraded datasets demonstrate the effectiveness of our proposed method. We compare our Uni-Removal framework with state-of-the-art supervised and unsupervised methods, showcasing its promising results in real-world image dehazing, deraining, and deblurring simultaneously.

翻译：从真实图像中去除多种退化现象（如雾、雨和模糊）是一项具有挑战性且不适定（ill-posed）的问题。近年来，能够处理不同退化类型的统一模型已被提出并取得了令人瞩目的成果。然而，这些方法主要针对合成图像，当应用于真实图像时性能显著下降。本文提出Uni-Removal，一种两阶段半监督框架，旨在通过统一模型与参数解决真实图像中的多重退化去除问题。在知识迁移阶段，Uni-Removal利用有监督的多教师-学生架构，促进从专精不同退化类型的预训练教师网络中学习。我们引入多粒度对比损失，以增强从特征空间和图像空间的学习效果。在领域自适应阶段，通过结合对抗判别器对真实图像进行无监督微调。扩展的多粒度对比损失与生成对抗损失的整合，使学生网络能够从合成域适应至真实域。在真实退化数据集上的大量实验验证了所提方法的有效性。我们将Uni-Removal框架与当前最先进的有监督及无监督方法进行对比，展示了其在真实图像去雾、去雨及去模糊任务中同时取得的前景性成果。